rename a struct in tests

test/integration/static_algorithms.jl

@@ -69,24 +69,24 @@ end
 # This a variation of `Selector` above that stores the names of rejected features in the
 # output of `fit`, for inspection by an accessor function called `rejected`.
 
-struct Selector2
+struct FancySelector
     names::Vector{Symbol}
 end
-Selector2(; names=Symbol[]) =  Selector2(names) # LearnAPI.constructor defined later
+FancySelector(; names=Symbol[]) =  FancySelector(names) # LearnAPI.constructor defined later
 
-mutable struct Selector2Fit
-    algorithm::Selector2
+mutable struct FancySelectorFitted
+    algorithm::FancySelector
     rejected::Vector{Symbol}
-    Selector2Fit(algorithm) = new(algorithm)
+    FancySelectorFitted(algorithm) = new(algorithm)
 end
-LearnAPI.algorithm(model::Selector2Fit) = model.algorithm
-rejected(model::Selector2Fit) = model.rejected
+LearnAPI.algorithm(model::FancySelectorFitted) = model.algorithm
+rejected(model::FancySelectorFitted) = model.rejected
 
 # Here we are wrapping `algorithm` with a place-holder for the `rejected` feature names.
-LearnAPI.fit(algorithm::Selector2; verbosity=1) = Selector2Fit(algorithm)
+LearnAPI.fit(algorithm::FancySelector; verbosity=1) = FancySelectorFitted(algorithm)
 
 # output the filtered table and add `rejected` field to model (mutatated!)
-function LearnAPI.transform(model::Selector2Fit, X)
+function LearnAPI.transform(model::FancySelectorFitted, X)
     table = Tables.columntable(X)
     names = Tables.columnnames(table)
     keep = LearnAPI.algorithm(model).names
@@ -98,15 +98,15 @@ function LearnAPI.transform(model::Selector2Fit, X)
 end
 
 # fit and transform in one step:
-function LearnAPI.transform(algorithm::Selector2, X)
+function LearnAPI.transform(algorithm::FancySelector, X)
     model = fit(algorithm)
     transform(model, X)
 end
 
 # note the necessity of overloading `is_static` (`fit` consumes no data):
 @trait(
-    Selector2,
-    constructor = Selector2,
+    FancySelector,
+    constructor = FancySelector,
     is_static = true,
     tags = ("feature engineering",),
     functions = (
@@ -120,7 +120,7 @@ end
 )
 
 @testset "test a variation that reports byproducts" begin
-    algorithm = Selector2(names=[:x, :w])
+    algorithm = FancySelector(names=[:x, :w])
     X = DataFrames.DataFrame(rand(3, 4), [:x, :y, :z, :w])
     model = fit(algorithm) # no data arguments!
     @test !isdefined(model, :reject)